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Racial Faces in-the-Wild: Reducing Racial Bias by Information Maximization Adaptation Network
Mei Wang, Weihong Deng, Jiani Hu +2
Racial bias is an important issue in biometric, but has not been thoroughly studied in deep face recognition. In this paper, we first contribute a dedicated dataset called Racial F…
Virtual Class Enhanced Discriminative Embedding Learning
Binghui Chen, Weihong Deng, Haifeng Shen
Recently, learning discriminative features to improve the recognition performances gradually becomes the primary goal of deep learning, and numerous remarkable works have emerged.…
Learning Better Features for Face Detection with Feature Fusion and Segmentation Supervision
Wanxin Tian, Zixuan Wang, Haifeng Shen +6
The performance of face detectors has been largely improved with the development of convolutional neural network. However, it remains challenging for face detectors to detect tiny,…
ALMN: Deep Embedding Learning with Geometrical Virtual Point Generating
Binghui Chen, Weihong Deng
Deep embedding learning becomes more attractive for discriminative feature learning, but many methods still require hard-class mining, which is computationally complex and performa…
Deep Facial Expression Recognition: A Survey
Shan Li, Weihong Deng
With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in va…
Deep Face Recognition: A Survey
Mei Wang, Weihong Deng
Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction. This emerging technique has reshaped the research land…
Deep Visual Domain Adaptation: A Survey
Mei Wang, Weihong Deng
Deep domain adaption has emerged as a new learning technique to address the lack of massive amounts of labeled data. Compared to conventional methods, which learn shared feature su…